Main
Marina Vabistsevits
Doctoral student at the University of Bristol, working in the interdisciplinary research of applying data mining methods to answer epidemiological questions
Education
PhD, Data mining in Epidemiology
University of Bristol
Bristol, UK
2023 - 2019
- Thesis: “Data mining breast cancer epidemiological relationships”
MSc, Bioinformatics
University of Copenhagen
Copenhagen, Denmark
2017 - 2015
- Master’s thesis: “Identification of autophagy signatures in breast cancer using The Cancer Genome Atlas data”
BSc, Biochemistry
University of Bath
Bath, UK
2015 - 2011
- Final year project: “Structural analysis of substrate binding in Sulfolobus solfataricus 2-keto-3-deoxygluconate aldolase variants”
Research Experience
Doctoral Student
MRC Intergative Epidemiology Unit
University of Bristol
2023 - 2019
- Mini-project 1: Implemented a generalisable R workflow of performing multivariate correlation analysis (metaCCA) on several GWAS, using summary statistics from MR-Base, in order to find pleiotropic variants across the measured traits
- Mini-project 2: Carried out a Mendelian Randomisation study to investigate the mechanism mediating the effect of early life BMI on breast cancer risk
- Main PhD project: Working with EpiGraphDB, a Neo4j graph database, to answer causal relationship questions in breast cancer and build a comprehensive model of the disease aetiology, by applying data mining and machine learning methods
Visiting Researcher / Master’s Thesis Student
Danish Cancer Research Centre
Copenhagen, Denmark
2017 - 2016
- Explored TCGA breast cancer gene expression RNA-Seq data to identify the involvement of autophagy-related genes in certain disease subtypes.
- Performed an extensive EDA, followed by differential expression and enrichment analyses, allowing me to find over-represented autophagy genes
- Built an efficient workflow in R, and identified and applied a new method to this field, which was enthusiastically adopted by other group members
Industry Experience
Data Scientist (Senior Executive Officer)
Public Health England
UK
2021 - 2020
- Joined Contact Tracing and Surveillance team as an R developer to work on COVID-19 Test & Trace data analysis
Bioinformatician
Living DNA
Frome, UK
2020 - 2017
- Led the research work on improving the ancestry reference panels used by the core pipeline behind the company’s direct-to-consumer ancestry genetics test, bringing considerable improvement to results accuracy
- Gained experience in working with a legacy codebase through maintaining and contributing to the in-house pipelines (Python)
- Honed my R programming skills by switching to tidyverse approach and advanced my data visualisation skills
- In 2019, left the full-time role to pursue further education, but continued contributing to the research and development at Living DNA on a part-time basis
Student research assistant in the Big Data group
3Shape
Copenhagen, Denmark
2017 - 2016
- Performed data preparation and visualisation tasks in Python, gaining practical experience of programming in a professional environment
- Used deep learning framework Caffe2 to develop a neural network training pipeline for scan image classification tasks
Placement Student in Bioinformatics team
Oxford Gene Technology
Oxford, UK
2014 - 2013
- Became responsible for a multitude of exome- and RNA-seq projects, running in-house data analysis pipelines and performing custom analysis for different projects
Extra contributions
COVID-19 data analytics Hackathon
by TrueCue and Women in Data
N/A
2020
Food Standards Agency Data Visualisation Challenge
by Jean Golding Institute and FSA
Runner-up prize
2020
Air pollution in Bath hackathon with Bath:Hacked group
by BreATHe project by B&NES Council
N/A
2018
Hackathon for Dogs Trust charity
by Microsoft and R-Ladies London
N/A
2018
Hackathon for Danish Cancer Society
by Deloitte and Danish Cancer Society
Best Technical solution Award
2017